updated the documentation, waiting for the pymadpl issue to be resolved.

examples/00-basic/00-cycles-to-failure.py

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+"""
+
+DPF-Core Fatigue Engineering Simple Example
+~~~~~~~~~~~~~~~~~~~~
+This example shows how to generate and use a result file to calculate the 
+cycles to failure result for a simple model.
+
+Material data is manually imported, Structural Steel from Ansys Mechanical:
+Youngs Modulus (youngsSteel)
+Poisson's Ratio (prxySteel)
+Cycles to Failure (snData)
+
+The first step is to generate a simple model with high stress and save the 
+results .rst locally to myDir (default C:\temp).
+For this we use a short pyMapdl script
+
+The second step uses DPF-Core to generate the cycles to failure result.
+The locally saved rst is imported and plotted.
+Then the von Mises stress is generated and plotted with DPF operators.
+The python package numpy is then used to interpolate the cycles to failure values.
+The nodal von Mises equivalent stress value is used in the interpolation.
+Note the cycles to failure data needs to be manipulated to use numpy interpolation.
+
+An empty field is created and filled with the resulting cycles to failure values.
+Cycles to failure result plotted.
+
+The cycles to failure result is the (interpolated) negative of the stress result.
+The higher the stress result, the lower the number of cycles to failure.
+
+Start MAPDL as a service to generate the rst file
+and import the DPF-Core module as ``dpf_core``.
+"""
+
+
+from ansys.mapdl.core import launch_mapdl
+from ansys.dpf import core as dpf
+import numpy as np
+myDir = r'c:\temp'
+
+## Material parameters from Ansys Mechanical - Structural Steel
+youngsSteel = 200e9
+prxySteel = 0.3
+snData = np.empty((11, 2)) # initialize empty np matrix
+snData[:, 0] = [10, 20, 50, 100, 200, 2000, 10000, 20000, 1e5, 2e5, 1e6]
+snData[:, 1] = [3.999e9, 2.8327e9, 1.896e9, 1.413e9, 1.069e9, 4.41e8, 2.62e8, 2.14e8, 1.38e8, 1.14e8, 8.62e7]
+
+#### Launch pymapdl to generate rst file in myDir
+mapdl = launch_mapdl(run_location=myDir)
+mapdl.prep7()
+# Model
+mapdl.cylind(0.5, 0, 10, 0)
+mapdl.mp("EX", 1, youngsSteel)
+mapdl.mp("PRXY", 1, prxySteel)
+mapdl.mshape(key = 1, dimension = '3d')
+mapdl.et(1, "SOLID186")
+mapdl.esize(0.3)
+mapdl.vmesh('ALL')
+
+# #### Boundary Conditions: fixed constraint
+mapdl.nsel(type_='S', item='LOC', comp ='Z', vmin = 0)
+mapdl.d("all", "all")
+mapdl.nsel(type_='S', item='LOC', comp ='Z', vmin = 10)
+nnodes = mapdl.get("NumNodes" , "NODE", 0, "COUNT" )
+mapdl.f(node = "ALL", lab = "fy", value =  -13e6/nnodes)
+mapdl.allsel()
+
+# #### Solve
+mapdl.run("/SOLU")
+sol_output = mapdl.solve()
+mapdl.exit()
+print('apdl model solved.')
+
+# ##### pydpf is used to post process the .rst in order to estimate the cycles to failure
+model = dpf.Model(myDir + '\\file.rst')
+print(model)
+mesh = model.metadata.meshed_region
+
+# Get the von mises equivalent stress, requires an operator
+s_eqv_op = dpf.operators.result.stress_von_mises()
+s_eqv_op.inputs.data_sources.connect(model)
+vm_stress_fields = s_eqv_op.outputs.fields_container()
+vm_stress_nodal = vm_stress_fields[0]
+vm_stress_nodal.plot()
+
+# Use numpy to interpolate the results.
+vm_stress = vm_stress_nodal.data
+myNodes = vm_stress_nodal.scoping.ids
+xPoints = snData[:, 1][::-1] # the x values are the stress ranges in ascending order
+yValues = snData[:, 0][::-1] # y values are inverted cycles to failure
+
+myResult = np.ones((len(myNodes), 3))
+myResult[:, 0] = myNodes
+myResult[:, 1] = vm_stress
+myResult[:, 2] = np.interp(myResult[:, 1], xPoints, yValues)
+
+##Create an empty field, add nodes/results and plot
+myResultField = dpf.Field(len(myNodes), dpf.natures.scalar, 'Nodal')
+my_scoping = dpf.Scoping()
+my_scoping.location = 'Nodal'
+my_scoping.ids = myResult[:, 0]
+myResultField.scoping = my_scoping
+myResultField.data =  myResult[:, 2]
+mesh.plot(myResultField)